Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. The explicit simulation of these effects with advanced snow radiative transfer models is associated with a large computational cost. Consequently, many albedo schemes used in snowpack models still rely on empirical parameterizations that do not account for the spatial variability of LAP deposition. In this study, a new strategy of intermediate complexity that includes the effects of spatially variable LAP deposition on snow albedo is tested with the snowpack model Crocus. It relies on an optimization of the parameter that controls the evolution of snow albedo in the visible range. Optimized values for multi-year snow albedo simulations with Crocus were generated at ten reference experimental sites spanning a large variety of snow climates across the world. A regression was then established between these optimal values and climatological deposition of LAP on snow at the location of the experimental sites extracted from a global climatology developed in this study. This regression was finally combined with the global climatology to obtain a LAP-informed and spatially variable parameter for the Crocus albedo parameterization. The revised parameter improved snow albedo simulations at the ten sites by 10% with the largest improvements found in the Arctic (more than 25%). The impact of this new albedo parameterization was then tested for multi-year gridded snowpack simulations over North America. Results highlighted significant improvements in terms of snow cover duration and melt-out date.